A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Electronics, Год журнала: 2023, Номер 12(18), С. 3960 - 3960

Опубликована: Сен. 20, 2023

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to complexity stock market, establishing effective methods facing challenges from various aspects because market. Existing research has inadequately utilized news information, overlooking significant details within content. By constructing a deep hybrid model for comprehensive analysis historical trading data and complemented by momentum strategies, this paper introduces novel approach. For first time, we fully consider two dimensions news, including headlines contents, further explore their combined impact on modeling price. Our approach initially employs fundamental screen valuable stocks. Subsequently, built technical factors based data. We then integrated content summarized through language models extract semantic information representations. Lastly, constructed neural capture global features combining with representations, enabling prediction decisions. Empirical results conducted over 4000 stocks Chinese market demonstrated that incorporating enriched enhanced objectivity sentiment analysis. proposed method achieved annualized return rate 32.06% maximum drawdown 5.14%. It significantly outperformed CSI 300 index, indicating its applicability guiding investors making more strategies realizing considerable returns.

Язык: Английский

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Machine Learning Models-Based Forecasting Moroccan Stock Market

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 56 - 66

Опубликована: Янв. 1, 2024

Язык: Английский

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Stock Price Analysis and Prediction Using Seq2Seq LSTM

Lecture notes in networks and systems, Год журнала: 2023, Номер unknown, С. 655 - 666

Опубликована: Янв. 1, 2023

Язык: Английский

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Unveling the Precision of Deep Learning Models for Stock Price Prediction: A Comparative Analysis of Bi-LSTM, LSTM, and GRU

Опубликована: Окт. 25, 2023

Due to the unpredictability of stock market, accurate prognostic models are necessary for investing. In recent years, machine learning techniques, specifically deep algorithms, have grown in popularity predicting prices. This paper seeks compare stock-price forecasting abilities several models, including LSTM, Bi-LSTM, and GRU. The algorithms make use capabilities Recurrent Neural Networks (RNNs), with a particular emphasis on Long-Short Term Memory (LSTM) model. primary objective is evaluate accuracy these at market values determine how number training epochs affects model performance. Through comparative analysis, we intend identify most Using historical data, research involves evaluating various models. Common evaluation metrics, such as Root Mean Square Error (RMSE), Squared (MSE), Absolute (MAE), used performance each terms RMSE, MSE, MAE, bi-LSTM outperforms other obtaining 0.00029, 0.01 respectively.

Язык: Английский

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Stock Market Forecasting using ANN

2021 5th International Conference on Information Systems and Computer Networks (ISCON), Год журнала: 2023, Номер unknown, С. 1 - 5

Опубликована: Март 3, 2023

Due to the unpredictable nature of share market, prediction market is an assignment. However, as a way recognize or make earnings, numerous marketplace contributors researchers try forecast percentage price by use diverse numerical, related finance even neural community approaches. Herein paper, effort made approximately proportion using Artificial Neural Network (ANN) this approach strong and consistent.

Язык: Английский

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Neural Network and Sentimental Model for Prediction of Stock Trade Value

Revue d intelligence artificielle, Год журнала: 2023, Номер 37(2), С. 315 - 321

Опубликована: Апрель 30, 2023

Forecasting and pattern recognition are increasingly important in unpredictable of the stock market.No system can consistently deliver correct predictions; complex machine learning approaches required.Many research initiatives from numerous disciplines have been carried out to address difficulties market forecasting.In order predict values, a significant amount has conducted.Many techniques applied this form forecasting, results were satisfactory.In study, we'll utilize web scraping get all actual data National Stock Exchange (NSE) Long Short Term Memory (LSTM) Networks with prior mining try forecast value on certain day.The study show potential LSTM for examining historical price obtaining useful guidance through trend forecasting appropriate economic parameters.To determine if company's is heading upward or lower, should also gather most recent commentary pertinent websites apply noise reduction, classifier, an algorithm analyze sentiment polarity.Using method, proposed represents current condition specific information.

Язык: Английский

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An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models

Опубликована: Ноя. 23, 2023

The OHLCV (Open, High, Low, Close, Volume) data used in this study is to forecast time series and anticipate stock price movement. We investigate a wide variety of models, including traditional statistical approaches cutting-edge deep learning strategies combined with sentiment analysis, feature extraction, hyperparameter tweaking. Instead focusing on absolute prices, our main goal predict swings as has been shown produce more accurate outcomes. start research by obtaining historical Amazon via the Yahoo API, then we go thorough analytical journey. generate features first, design test Fourier Autoregressive Integrated Moving Average (ARIMA) models. switch sophisticated methods, using pre-processed apply Long Short-Term Memory (LSTM) Interestingly, add analysis LSTM study, which expands its scope lets us consider market possible influencing factor. To guarantee stability use careful train-test split technique organize manner. field financial forecasting trading methods will ultimately benefit from insightful information study's findings provide efficacy different modeling techniques their capacity movements.

Язык: Английский

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ANFIS-Based Investment Recommendations for Government Bonds: Personalized Approach

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 3 - 20

Опубликована: Янв. 1, 2024

Язык: Английский

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A multi-scale multi-head attention network for stock trend prediction considering textual factors

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112388 - 112388

Опубликована: Окт. 1, 2024

Язык: Английский

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Prediction of the Price of Advanced Global Stock Markets Using Machine Learning: Comparative Analysis

Journal of Financial Risk Management, Год журнала: 2024, Номер 13(04), С. 689 - 702

Опубликована: Янв. 1, 2024

Язык: Английский

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